Python深度指南：构建高效机器学习系统

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"《用Python构建机器学习系统》是一本由Willi Richert和Luis Pedro Coelho合著的专业书籍，两位作者分别在机器学习和机器人学领域有着深厚的背景。Willi Richert是微软Bing搜索的核心研发团队成员，他的研究专长涵盖了主动学习和统计机器翻译，这使得他在指导如何利用Python进行高级机器学习系统开发方面具有丰富的实践经验。Luis Pedro Coelho则是一位计算生物学家，专注于生物图像信息学和大规模图像数据处理，他在生物标本图像分析中的机器学习技术应用尤其突出，同时也是Python计算机视觉库mahotas的主要开发者。两位作者都拥有深厚学术背景，世界顶级的卡内基-梅隆大学的机器学习博士学位，以及丰富的科研成果。本书旨在帮助读者掌握Python在机器学习领域的核心技术，通过深入浅出的手把手教程，引导读者构建高效且实用的机器学习系统。它适合那些希望在Python环境下深化机器学习理解并将其应用于实际项目的专业人士，不论他们是在学术界进行研究，还是在工业界寻求创新解决方案。作为一本版权作品，本书受到严格的版权保护，未经Packt Publishing事先书面许可，不得复制、存储或以任何形式传播书中的内容。尽管出版方已尽最大努力确保信息的准确性，但书中提供的所有信息均按现状销售，不承担因使用本书内容导致的任何直接或间接损害的责任。 Packt Publishing在书中尽可能地提供了关于所提及公司和产品的商标信息，但在某些情况下，可能无法做到详尽无遗。《用Python构建机器学习系统》是一本权威且实用的资源，对于任何希望利用Python进行机器学习研究和实践的读者来说，都是一个宝贵的工具和指南。"

Preface

You could argue that it is a fortunate coincidence that you are holding this book in

your hands (or your e-book reader). After all, there are millions of books printed

every year, which are read by millions of readers; and then there is this book read by

you. You could also argue that a couple of machine learning algorithms played their

role in leading you to this book (or this book to you). And we, the authors, are happy

that you want to understand more about the how and why.

Most of this book will cover the how. How should the data be processed so that

machine learning algorithms can make the most out of it? How should you choose

the right algorithm for a problem at hand?

Occasionally, we will also cover the why. Why is it important to measure correctly?

Why does one algorithm outperform another one in a given scenario?

We know that there is much more to learn to be an expert in the eld. After all, we only

covered some of the "hows" and just a tiny fraction of the "whys". But at the end, we

hope that this mixture will help you to get up and running as quickly as possible.

What this book covers

Chapter 1, Getting Started with Python Machine Learning, introduces the basic idea

of machine learning with a very simple example. Despite its simplicity, it will

challenge us with the risk of overtting.

Chapter 2, Learning How to Classify with Real-world Examples, explains the use of

real data to learn about classication, whereby we train a computer to be able to

distinguish between different classes of owers.

Chapter 3, Clustering – Finding Related Posts, explains how powerful the

bag-of-words approach is when we apply it to nding similar posts without

really understanding them.

Preface

[ 2 ]

Chapter 4, Topic Modeling, takes us beyond assigning each post to a single cluster

and shows us how assigning them to several topics as real text can deal with

multiple topics.

Chapter 5, Classication – Detecting Poor Answers, explains how to use logistic

regression to nd whether a user's answer to a question is good or bad. Behind

the scenes, we will learn how to use the bias-variance trade-off to debug machine

learning models.

Chapter 6, Classication II – Sentiment Analysis, introduces how Naive Bayes

works, and how to use it to classify tweets in order to see whether they are

positive or negative.

Chapter 7, Regression – Recommendations, discusses a classical topic in handling

data, but it is still relevant today. We will use it to build recommendation

systems, a system that can take user input about the likes and dislikes to

recommend new products.

Chapter 8, Regression – Recommendations Improved, improves our recommendations

by using multiple methods at once. We will also see how to build recommendations

just from shopping data without the need of rating data (which users do not

always provide).

Chapter 9, Classication III – Music Genre Classication, illustrates how if someone has

scrambled our huge music collection, then our only hope to create an order is to let

a machine learner classify our songs. It will turn out that it is sometimes better to

trust someone else's expertise than creating features ourselves.

Chapter 10, Computer Vision – Pattern Recognition, explains how to apply classications

in the specic context of handling images, a eld known as pattern recognition.

Chapter 11, Dimensionality Reduction, teaches us what other methods exist

that can help us in downsizing data so that it is chewable by our machine

learning algorithms.

Chapter 12, Big(ger) Data, explains how data sizes keep getting bigger, and how

this often becomes a problem for the analysis. In this chapter, we explore some

approaches to deal with larger data by taking advantage of multiple core or

computing clusters. We also have an introduction to using cloud computing

(using Amazon's Web Services as our cloud provider).

Appendix, Where to Learn More about Machine Learning, covers a list of wonderful

resources available for machine learning.

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